Columbia University

What you'll study

The programme combines a core set of theoretical topics with applied coursework and project work. Core subjects typically include linear and nonlinear optimisation, convex analysis, stochastic processes, probability and statistics, simulation, and numerical methods. Students also study specialised areas such as network flows, queuing theory, dynamic programming, stochastic optimisation, statistical learning and machine learning approaches for decision-making.

Instruction emphasises both mathematical foundations and computational implementation. Coursework commonly involves coursework-based projects and a capstone experience or supervised research/thesis option where students apply models to applied problems (for example supply-chain optimisation, revenue management, portfolio optimisation, healthcare operations, or large-scale data analytics).

• Core modules: Mathematical optimisation, Probability & stochastic processes, Numerical methods for optimisation.
• Typical electives: Network optimisation and routing, Stochastic modelling and control, Simulation and applied probability, Machine learning for decision-making, Operations management and supply chains.
• Capstone/research: Industry project, practicum or a master's thesis supervised by faculty in the Department of Industrial Engineering and Operations Research (IEOR).
• Computing and software: Emphasis on programming for optimisation and analytics (Python, R, MATLAB, optimization solvers, and data-engineering tools).

Entry requirements

Applicants are expected to have a strong quantitative undergraduate degree in engineering, mathematics, statistics, computer science, physics, economics or a related field. Successful applicants typically demonstrate competency in multivariable calculus, linear algebra, probability and basic programming.

• Academic transcripts showing a strong quantitative background.
• A personal statement outlining academic and professional objectives and fit with the programme.
• Letters of recommendation from academic or professional referees who can speak to analytical ability and potential for graduate study.
• A current résumé or CV detailing relevant coursework, projects and work experience.
• Proof of English language proficiency where required by university policy.

Standardised test requirements and other documentary details vary; applicants should consult the department’s admissions page for the most up-to-date guidance.

Career prospects

Graduates of the programme go on to analytical and technical roles across many sectors. Common job titles include operations research analyst, data scientist, quantitative analyst, optimisation engineer, supply-chain analyst and management consultant. The training is also a solid foundation for roles in finance (quantitative trading, risk management), technology (algorithms, platforms, machine learning systems), healthcare analytics, transportation and logistics, and government or public policy analytics.

Alumni typically find employment with major technology companies, consultancies, financial institutions, logistics and manufacturing firms, healthcare organisations and research labs. The programme also prepares students for doctoral study in operations research, applied mathematics, statistics or related fields.

Why study at Columbia University

Columbia’s Department of Industrial Engineering and Operations Research (IEOR) is one of the established centres for theory and application in optimisation, stochastic modelling and data-driven decision-making. Studying at Columbia gives access to faculty who combine foundational research with collaborations across finance, healthcare, law, and technology, and to interdisciplinary opportunities throughout the university.

Located in New York City, the programme benefits from proximity to a dense ecosystem of industry partners and employers, internship opportunities and a large alumni network. Students have access to research centres, high-performance computing resources and a curriculum that balances rigorous theory with practical, project-based experience.